Download Multiplexed Infectious Disease Analysis

WH ITEPAPER
Multiplexed Infectious
Disease Analysis
Using nCounter® for Simultaneous Pathogen
and Host Gene Expression Profiling
v1.1 | December 2016
NanoString Technologies, Inc., Seattle, WA 98109
Authors: Wenjie Xu and Joseph M. Beechem
Multiplexed Infectious Disease Pathogen
and Host Profiling Using nCounter
Introduction
Infectious disease researchers face critical challenges in their
efforts to better understand how pathogens function and how
hosts respond. Infection samples are scarce, complex, and usually
dominated by host origin cells; pathogens typically constitute only a
tiny fraction. Efficient techniques are needed to track transcriptional
profiles in vivo, where gene expression may differ significantly from
patterns observed in vitro, and to assess the host’s immune response.
Finally, current clinical methods of identifying pathogens and
determining antibiotic resistance are too slow, causing many
patients to receive ineffective treatment.
NanoString’s nCounter platform provides a simple workflow to
enable rapid, precise, and cost-effective infectious disease research
and diagnosis. This direct hybridization-based digital counting
technology offers several key advantages:
•
Easy sample prep that involves no enzymes or amplification
•
Compatible with complex input, including direct lysates from
various tissue types
•
Quantifies up to 800 targets simultaneously from multiple
pathogens and host cells
•
Highly reproducible data that does not require technical replicates
•
15 minutes of hands-on time; sample to data in less than 24 hours
•
Straightforward automated “biology-guided” data analysis with
free nSolver software package
Scientists around the world are taking advantage of nCounter to
answer a wide range of infectious disease research questions. Teams
have used NanoString technology to study the bacterial and viral
pathogens involved in acute respiratory tract infections, the in vivo
expression profile of the major human fungal pathogen Candida
albicans, the host’s inflammatory response to West Nile virus across
multiple tissues, and changes in T cell function in tuberculosis.
Others are pursuing diagnostic applications: for instance, one group
has analyzed microRNA patterns to improve detection of human
enterovirus 71, and another team is developing tests to quickly identify
pathogens and assess antibiotic resistance in blood infections.
Identifying Multiple Bacterial and Viral Species in a Single Assay
Researchers probe for a range of pathogens in acute respiratory
tract infections
Camila I. de Oliveira, an infectious disease researcher at the Oswaldo
Cruz Foundation (Fiocruz) in Salvador, Brazil, and her colleagues
wanted to characterize the pathogens involved in acute respiratory
2 | Introduction JA N UA RY 2 2 , 2 01 7
tract infections in local patients. These infections commonly affect
children less than five years old and can be fatal if they progress to
pneumonia. Although many viruses and bacteria had been connected
to this illness, studies tended to focus on one type of pathogen at a
time. “We don’t see many studies that describe the many pathogens
that can be present,” de Oliveira says.
Working with physicians at the Federal University of Bahia in Salvador,
de Oliveira’s team obtained samples of nasopharyngeal aspirates from
young patients. Initially, the researchers planned to test for pathogens
using RT-PCR. But when the clinicians suggested running a large
panel to detect many species, she realized that this approach was
impractical. “When we got the list of pathogens, we said, ‘This is not
possible to do by RT-PCR,’” she recalls. “It’s just going to be too much.”
Instead, the team used NanoString’s nCounter platform to
simultaneously test for more than a dozen bacteria and viruses,
including rhinovirus, parainfluenza, Staphylococcus aureus, and
Haemophilus influenzae. With collaborators at the Rega Institute for
Medical Research in Leuven, Belgium, the researchers found they could
accurately identify the species in samples spiked with pathogen RNA;
the nCounter method gave results similar to those obtained by realtime PCR experiments. The team then probed 61 patient samples and
found that 55% contained only bacteria, 44% had both bacteria and
viruses, and 1.4% had only viruses. The results were published in 2015 in
the Journal of Clinical Virology1.
FIGURE 1: Simultaneous detection of multiple pathogens in acute
respiratory tract infection (ARI) patients. nCounter was used to detect 13
types of viruses and six bacteria species in nasopharyngeal aspirates from 61
ARI patients and seven healthy controls (HC).
Darker colors correspond to higher probe counts.
Reprinted from Journal of Clinical Virology 69, Fukutani KF et al. “Pathogen
transcriptional profile in nasopharyngeal aspirates of children with acute
respiratory tract infection.” 190-196, 2015, with permission from Elsevier.
The researchers are now expanding the analysis to about 600 samples
and hope to correlate specific pathogens with clinical outcomes
and patient demographic data, such as socio-economic factors.
De Oliveira’s team also has used nCounter on the same samples to
assess patients’ immune responses. The technology delivered quick
results on many targets without generating an overwhelming amount
of data. It was “very informative for us,” she says. “We had a lot of
parameters being detected in the same samples, all at once.”
Further Reading: A team at the Stanford University School of
Medicine in California assessed nCounter’s ability to identify and
quantify 10 fungal species. The results show that “an amplificationfree technology can detect multiple fungal pathogens at the species
level with acceptable specificity, sensitivity, and reproducibility
within a 24-hour turnaround time,” the researchers wrote in
Diagnostic Microbiology and Infectious Disease2.
Studying a Fungal Pathogen’s Gene Expression In Vivo
Analysis reveals Candida albicans’ response to drug treatment
The fungal pathogen Candida albicans often causes bloodstream
infections in hospitalized patients, particularly those who have
AIDS or are undergoing chemotherapy. Many studies of the fungus’
gene expression have been performed in vitro. But Wenjie Xu, a
microbiologist who studied C. albicans at Carnegie Mellon University
in Pittsburgh, Pennsylvania, decided to investigate how the pathogen
behaved in the host environment. “To understand how the pathogen
survives inside the host and how it responds to a drug, we have to do
it in vivo,” says Xu, now a technical services scientist at NanoString.
Studying in vivo samples is challenging because more than 99% of the
RNA from infected tissue is from host cells. The host RNA contributes to
high background on microarrays and dominates sequence reads from
RNA-Seq. In contrast, NanoString’s nCounter system allowed Xu’s team
to easily work with mixed RNA, and the workflow was simple and fast
enough for the scientists to process a large number of samples from a
time course study.
Using nCounter in Infectious Disease Research: A Step-by-Step
Video Guide
FIGURE 2: Stages of Candida albicans gene expression in vivo from 0 to
48 hours after infection. Researchers used NanoString technology to track
248 environmentally responsive genes in kidney samples from infected
mice. Fully saturated color represents a 10-fold change in expression.
Xu W et al. (2015) PLOS Biology 13(2):e1002076. CC BY 4.0:
https://creativecommons.org/licenses/by/4.0/legalcode.
The researchers reviewed the existing literature to select 248
environmentally responsive genes that were suspected to play critical
roles during infection. Next, the team collected kidney samples from
mice at 0, 12, 24, and 48 hours after infection and assayed them for
gene expression. The same samples also were tested for 231 C. albicans
transcription factors and 46 mouse genes involved in responding to
fungal infections. “We have perfectly matched host immune response
and pathogen expression profiling from the exact same RNA prep,” Xu
says. “We have two sides of the same story.”
Xu and his colleagues found that the pathogen and host’s gene
expression changed over the course of infection. For example, fungal
genes upregulated in the early stage were related to nutritional
limitation and hyphal growth, while genes upregulated later were
involved in oxidative stress. When the team treated the mice with
caspofungin, a drug commonly given to patients, the fungus’ expression
profile in vivo differed substantially from responses reported in vitro.
Many of the induced genes overlapped with those that the pathogen
had downregulated during early infection, presumably to increase its
chances of survival.
Xu W et al. (2016) J. Vis. Exp. 107:e53460.
In the Journal of Visualized Experiments, NanoString scientist Wenjie Xu
and his colleagues provide a detailed guide for using nCounter to study
gene expression in infected tissues4. The article includes a step-by-step
video demonstrating the procedure.
“That’s intriguing because it’s almost like caspofungin knows the weak
points of the pathogen,” says Xu, who published the results in 2015 in
PLOS Biology3. By learning more about the drug’s mechanism of action,
researchers can identify pathways to target with new treatments.
3 | Identifying Multiple Bacterial and Viral Species in a Single Assay JANUARY 22, 2017
Further reading: A team at the Harvard School of Public
Health in Boston analyzed in vivo gene expression of the
parasite Plasmodium falciparum in tissues of children in
Malawi who had died from malaria. The researchers wrote
in Genome Medicine that the nCounter platform “is of great
utility to the malaria community, as it is amenable to many
different types of samples”5. In another study, researchers at
the Dartmouth-Hitchcock Medical Center in Lebanon, New
Hampshire and their colleagues used NanoString technology
to track Pseudomonas aeruginosa transcripts in sputum
samples, noting that this method “avoids the potentially
confounding effects of in vitro culture conditions.”6
Using MicroRNAs to Diagnose Severe Viral Infections
Human enterovirus 71 infection is linked to elevation of specific
miRNAs
Patients may respond to pathogens by increasing or decreasing
levels of certain microRNAs. Since these patterns are indirectly linked
to infection, they could be used as a diagnostic tool to identify the
pathogen in clinical settings. Robert Wang, a molecular virologist at
Chang Gung University in Taoyuan, Taiwan, wanted to explore whether
miRNAs could help physicians determine if a patient with hand, foot,
and mouth disease is infected with human enterovirus 71 (EV71),
which can enter the central nervous system and lead to neurological
problems and death.
Wang chose to use NanoString’s nCounter platform to characterize
the miRNA patterns in EV71 infection because the system provides
quantitative data. Next-generation sequencing would require
amplification, which “can cause a lot of false positives,” he says.
His team studied serum samples from four patients with mild infections,
four with severe infections, and four healthy controls. After isolating
RNA, the researchers used the nCounter Human miRNA Expression
Assay Kit to detect 800 miRNAs. They found that levels of 44 miRNAs
were at least twice as high in infected patients as in controls, and levels
of 133 miRNAs were reduced by at least half. The experiments helped
them identify a miRNA, called miR876-5p, that was upregulated nearly
10-fold in severe cases, Wang and his colleagues reported in 2016 in
Scientific Reports7. Treating mice with an miR876-5p inhibitor before
infection lowered viral replication, suggesting that this miRNA plays
an important role in infection.
The work could eventually improve diagnosis and treatment. If
researchers can narrow down the set of upregulated miRNAs to a
smaller panel, clinicians could test for those miRNAs to determine
if a patient is likely to develop severe disease. Patients also could
potentially be treated with a miR876-5p inhibitor, Wang says. He is now
extending the analysis to more patients, and he has performed similar
studies using nCounter to identify miRNAs associated with Japanese
encephalitis virus infection. “NanoString provides me with a whole
picture of microRNA,” he says.
Further reading: In a study published in the Journal of Immunology,
a team at Ohio State University in Columbus investigated how
Mycobacterium tuberculosis alters host miRNA expression8. Using
nCounter, the researchers found 31 miRNAs that were significantly
upregulated or downregulated during infection, including two that
may weaken immune response.
FIGURE 3: MicroRNA expression profiles in patients infected with human enterovirus 71 (EV71). The nCounter Human miRNA Expression Assay Kit was
used to detect 800 miRNAs in EV71 infections. Levels in healthy controls (X axis) are plotted against levels in patients with mild or severe infections
(Y axis). Red indicates a significant increase, and green indicates a significant decrease.
Wang RYL et al. (2016) Sci. Reports 6:24149. CC BY 4.0: https://creativecommons.org/licenses/by/4.0/legalcode.
4 | Studying a Fungal Pathogen’s Gene Expression In Vivo JA NUARY 22, 2017
Investigating the Immune Response in Tuberculosis
nCounter experiment provides evidence for T cell exhaustion
In chronic Mycobacterium tuberculosis infections, bacterial
recrudescence can sometimes occur. “Suddenly there’s a breakthrough
of infection,” says Pushpa Jayaraman, an immunologist at the Novartis
Institutes for BioMedical Research in Cambridge. The immune system
“just kind of gives up.”
While she was working at the University of Massachusetts Medical
School in Worcester, Jayaraman and her colleagues investigated
whether the host’s T cells became functionally exhausted, a type of
immune failure that had been observed with other pathogens such
as HIV and hepatitis B and C viruses. “This is a very well-documented
phenomenon,” she says. “But it had not been shown for TB at all.” In
experiments with mice infected with M. tuberculosis, the team found
that the T cells showed typical signs of exhaustion: for instance, their
cytokine production declined, and they expressed inhibitory receptors
such as TIM3 and PD1.
But the researchers wanted to characterize the cells’ gene expression in
more detail. They turned to NanoString’s nCounter platform to measure
RNA levels of about 200 genes involved in processes such as T cell
activation and regulating transcription. The system allowed them to
gather data with high sensitivity while avoiding potential problems such
as preferential amplification. “It just gave us better data,” Jayaraman
says. The team also could expand their analysis beyond a few genes of
interest, but not to such a large set that it became difficult to see trends.
The nCounter experiment showed that the T cells had distinct
signatures depending on which inhibitory receptors they expressed.
“This gives us a bird’s-eye view of the differences,” she says. For
instance, cells expressing TIM3 but not PD1 showed higher transcription
of pro-inflammatory cytokine and chemokine receptor genes than
double-positive cells expressing both TIM3 and PD1 did. When the
researchers compared the profiles to a set of genes associated with T
cell exhaustion, they found that many of those genes showed similar
expression patterns in the double-positive cells. The results, published in
2016 in PLOS Pathogens, suggest that treatments might need to target
multiple inhibitory receptors to re-activate the T cells.
NanoString’s technology enabled the researchers to examine gene
expression patterns in the cells “in a more holistic manner,” Jayaraman
says. She also has used nCounter to study the functional profile of T
cells9 after vaccination and to analyze cytokine networks that may
regulate T cell responses.
Further reading: A team led by scientists at Aarhus University in
Denmark analyzed gene expression in bone marrow-derived cells
with nCounter to characterize the innate immune response to murine
gammaherpesvirus 6810. In studies of West Nile virus, researchers
used NanoString panels to identify a genetic signature for susceptible
patients11 and to characterize host immune-related gene expression in
the spleen12 and brain13 after infection.
FIGURE 4: Distinct gene expression patterns in T cells after Mycobacterium tuberculosis infection. NanoString analysis revealed that T cells from
infected mice have different transcriptional profiles depending on whether they express the inhibitory receptor TIM3, PD1, or both. The experiment
provided evidence that T cells become functionally exhausted during infection.
Jayaraman P et al. (2016) PLOS Pathogens 12(3):e1005490. CC BY 4.0: https://creativecommons.org/licenses/by/4.0/legalcode.
5 | Investigating the Immune Response in Tuberculosis JA N UA RY 22, 2017
A Thorough Analysis of Inflammatory Response
Researchers track gene expression in multiple tissues after West Nile
virus infection
West Nile virus (WNV) has spread to North and South America,
and infection can lead to encephalitis and long-term neurological
damage. José Peña, a virologist at Lawrence Livermore National
Laboratory in California, and his colleagues, were seeking a method
to track the inflammatory response to the pathogen in multiple
tissues. In the past, due to the cost and labor involved in gene
expression analysis, “most experiments focused on just spleen and
brain,” he says. “But if you get an infection, it’s not just localized to
one tissue.”
The researchers decided to use NanoString’s technology to profile
the host inflammatory response in the lung, liver, kidney, spleen,
and brain of infected mice. Collaborators at the University of Texas
Medical Branch in Galveston collected samples from the animals’
tissues each day for nine days. Peña’s team then extracted RNA and
analyzed expression of 179 genes involved in inflammation using the
nCounter GX Mouse Inflammation Kit.
The team chose the nCounter platform because it provided a digital,
quantitative analysis that did not require amplification. “It took
one of the variables out: Is this an artifact or not?” Peña says. The
method also was less labor-intensive than other techniques. The
researchers processed about 24 to 48 samples per day and received
the initial data within about two weeks of starting the experiment.
The study “yielded a pretty large dataset that we could analyze
internally without too much computational time,” he says.
The team found many gene expression patterns that could serve as
the basis for future studies. For example, expression levels of the
chemokine gene Cxcl10 and cytokine gene Il12b changed in most
tissues, the transcription factor Maff was upregulated in the lung,
and chemoattractant gene expression was reduced in the kidney.
“The nCounter system and complementary methods employed here
provide a powerful platform for detailed comparative analysis of the
kinetics and magnitude of host responses to WNV infection,” the
authors wrote in 2014 in PLOS Neglected Tropical Diseases.14
Further reading: Using the nCounter GX Human Inflammation
Kit, researchers at Stanford University in California studied the
expression of 184 inflammation-related genes in patients who had
been infected with hepatitis C virus and found that pegylated
interferon treatment had long-lasting effects15. An international
team from Brazil and the United States created a custom NanoString
codeset to analyze 98 inflammatory genes in patients infected with
the parasite Plasmodium vivax16.
Rapid Tests to Identify Pathogens and Assess Antibiotic Resistance
nCounter helps researchers analyze complex blood samples
Today, bacterial infections are typically diagnosed by culturing a sample
from the patient, running biochemical assays, and testing the pathogens
for growth in the presence of antibiotics to assess susceptibility.
6 | A Thorough Analysis of Inflammatory Response JA N UA RY 22, 2017
FIGURE 5: Expression of inflammation-related genes across multiple
tissues in response to West Nile virus. Gene expression networks for the
brain (A), spleen (B), and kidney (C) of infected mice were generated from
data obtained with the nCounter GX Mouse Inflammation Kit. Blue genes
are upregulated, and red genes are downregulated; black edges represent
positive correlations, and green edges represent negative correlations.
Peña J et al. (2014) PLOS Neglected Tropical Diseases 8(10):e3216. CC BY
4.0: https://creativecommons.org/licenses/by/4.0/legalcode
However, the process takes about 48 to 72 hours, and clinicians often
make treatment decisions before results are available. Researchers are
now working with NanoString to develop a rapid diagnostic method
to reduce the likelihood that patients are prescribed ineffective
antibiotics. “There are mortality costs with getting a slow answer,” says
Deborah Hung, an infectious disease researcher at the Broad Institute in
Cambridge, Massachusetts.
In a 2015 study published in Lab on a Chip, the team tested their
approach on blood samples17. Blood is particularly difficult to
analyze because it contains molecular factors that can interfere with
amplification, and the pathogen abundance may be very low. “It is very
challenging to detect pathogens from the blood,” says Jongyoon Han, a
bioengineer at the Massachusetts Institute of Technology in Cambridge,
who collaborated with Hung. But rapid diagnostics are critical for
bacterial blood infections because delaying the proper treatment can
lead to septic shock. That’s the situation “where you need to know the
answer the fastest,” Hung says.
Hung decided to use nCounter for the tests because the platform offers
a multiplex, hybridization-based solution that works with cell lysates.
“The beauty of NanoString is that you can do it on crude samples,” she
says. In addition, “NanoString is an easier rapid platform where you
don’t have to do as much optimization of every probe set.”
FIGURE 6: Detection of three bacteria species in blood samples. Blood
was inoculated with Escherichia coli, Klebsiella pneumoniae, and/or
Pseudomonas aeruginosa at 1000 CFU/mL (A) and 100 CFU/mL (B).
Pathogens were isolated from blood cells using a microfluidic device,
then analyzed with NanoString’s rRNA detection assay.
Reproduced from Hou HW et al. (2015) Lab on a Chip 15:2297-2307 with
permission of The Royal Society of Chemistry.
In the study, the researchers first separated the pathogens from blood
using a microfluidic device developed by Han’s team. By running the
sample through a spiral-shaped channel, the scientists could isolate the
bacteria from red and white blood cells based on cell size. Hung’s team
then used nCounter to test for RNA from Escherichia coli, Klebsiella
pneumoniae, and Pseudomonas aeruginosa and identified the species
at levels as low as 100 CFU/mL. The researchers also exposed E. coli
to the antibiotic ciprofloxacin and accurately determined each strain’s
susceptibility to the treatment based on RNA signatures.
and Drug Administration (FDA). Since this original FDA clearance,
three additional multiplexed gene expression assays have received
clearance on the nCounter platform to perform patient stratification
in cancer treatment clinical trials. As described in this report, multiple
studies have demonstrated that nCounter can accurately and
simultaneously detect multiple bacterial, viral, and fungal species, as
well as transcriptional profiles of antibiotic susceptibility. NanoString
experiments also could improve diagnosis by identifying immune
The test currently takes about eight hours, and the researchers are
collaborating with NanoString to reduce the time to a few hours. “The
whole field is trying to push toward rapid diagnostics,” Hung says.
In the future, infectious disease treatment may follow a trajectory similar
to that of cancer treatment. Cancer researchers previously focused on
developing drugs to target the tumor; now, many successful treatments
do not interact with the tumor-cells directly, but are designed to interact
with the host, by inducing the immune system to more effectively
eliminate cancer cells. Similarly, most infectious disease treatments
currently target the pathogen, but new drugs could potentially be
developed to modify the host immune response to better recognize
and destroy infectious agents. Just as NanoString’s nCounter PanCancer
Immune Profiling Panel has enabled researchers to effectively study the
immune response to tumors, other nCounter products can help track
the immune response to bacteria, viruses, fungi, and parasites, providing
insight into potential treatment pathways. The highplex capability of the
nCounter system allows for the flexibility of multiplexing both pathogen
and host-specific responses simultaneously, ushering-in a whole new
capability in the study of infectious disease at the basic-research,
translational-research, and (ultimately) diagnostic level.
Further reading: In a 2012 study in Proceedings of the National
Academy of Sciences, Hung’s team showed that nCounter could identify
many other pathogens in lysates, including Mycobacterium tuberculosis,
influenza virus, herpes simplex virus-2, HIV-1, the fungus Candida
albicans, and the parasite Plasmodium falciparum18.
The Future of nCounter: Advanced Diagnostics and Immune
Response Profiling
One of the strengths of utilizing nCounter technology for translational
research, is that one can utilize the identical reagents and
instrumentation also, for full diagnostic testing (after appropriate
studies/approvals). NanoString’s rapid, multiplex technology has
been deployed as a diagnostic tool in the clinic. In 2013, NanoString’s
Prosigna™ Breast Cancer Prognostic Gene Signature Assay, which uses
the nCounter platform, received 510(k) clearance from the U.S. Food
7 | A Thorough Analysis of Inflammatory Response JA N UA RY 22, 2017
response signatures for specific pathogens.
RE F E RENCES
1 Fukutani KF et al. (2015) Pathogen transcriptional profile in
nasopharyngeal aspirates of children with acute respiratory tract
infection. J. Clinical Virology 69:190-196.
2 Hsu JL et al. (2014) Application of a non-amplification-based
technology to detect invasive fungal pathogens. Diag. Microbio. Inf.
Disease 78:137-140.
3 Xu W et al. (2015) Activation and alliance of regulatory pathways in C.
albicans during mammalian infection. PLOS Biology 13(2):e1002076.
4 Xu W et al. (2016) Gene expression profiling of infecting microbes using
a digital bar-coding platform. J. Vis. Exp. 107:e53460.
5 Van Tyne D et al. (2014) Plasmodium falciparum gene expression
measured directly from tissue during human infection.
Genome Med. 6:110.
6 Gifford AH et al. (2016) The use of a multiplex transcript method for the
analysis of the Pseudomonas aeruginosa gene expression profiles in the
cystic fibrosis lung. Infect. Immun. doi:10.1128/IAI.00437-16.
7 Wang RYL et al. (2016) Elevated expression of circulating miR876-5p is
a specific response to severe EV71 infections. Sci. Reports 6:24149.
8 Ni B et al. (2014) Mycobacterium tuberculosis decreases human
macrophage IFN-γ responsiveness through miR-132 and miR-26a. J.
Immunology doi:10.4049/jimmunol.1400124.
9 Jayaraman P et al. (2016) TIM3 mediates T cell exhaustion during
Mycobacterium tuberculosis infection. PLOS Pathogens 12(3):e1005490.
11 Qian F et al. (2015) Systems immunology reveals markers of susceptibility
to West Nile virus infection. Clinical and Vaccine Immunology 22(1):6-16.
12 Green, R et al. (2016) Identifying protective host gene expression
signatures within the spleen during West Nile virus infection
in the collaborative cross model. Genomics Data doi: 10.1016/j.
gdata.2016.10.006.
13 Green, R. et al. (2016) Transcriptional profiles of WNV neurovirulence in
a genetically diverse collaborative cross population. Genomics Data doi:
10.1016/j.gdata.2016.10.005.
14Peña J et al. (2014) Multiplexed digital mRNA profiling of the
inflammatory response in the West Nile Swiss Webster mouse model.
PLOS Neglected Tropical Diseases 8(10):e3216.
15 Waldron PR and M Holodniy (2015) Peripheral blood mononuclear cell
gene expression remains broadly altered years after successful interferonbased hepatitis C virus treatment. J. Immunology Research 958231.
16Rocha BC et al. (2015) Type I interferon transcriptional signature in
neutrophils and low-density granulocytes are associated with tissue
damage in malaria. Cell Reports 13:2829-2841.
17 Hou HW et al. (2015) Direct detection and drug-resistance profiling of
bacteremias using inertial microfluidics. Lab on a Chip 15:2297-2307.
18Barczak AK et al. (2012) RNA signatures allow rapid identification
of pathogens and antibiotic susceptibilities. Proc. Nat. Acad. Sci.
109(16):6217-6222
10Sun C et al. (2015) Evasion of innate cytosolic DNA sensing by a
gammaherpesvirus facilitates establishment of latent infection. J.
Immunology doi:10.4049/jimmunol.1402495.
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